Download the entire proceedings as an Adobe PDF - Eastern Snow ...

ABSTRACT
231
63 rd EASTERN SNOW CONFERENCE
Newark, Delaware USA 2006
Shaped Solution Domains for Snow Properties
RAE A. MELLOH, 1 SALLY A. SHOOP, 2 AND BARRY A. COUTERMARSH 2
The objective of this work was to develop a method for distributing snow properties on a
landscape that is less dependent on extensive mass and energy balance modeling, yet provides a
realistic distribution of snow depth and density across the environmental gradients of elevation,
slope, azimuth, and forest type. In this paper, we present important progress toward development
of such an approach. A shaped solution domain was identified for snow depth, water equivalent,
and density. The pinched-cone shape describes the differentiation of the snow properties with
slope and azimuth and is approximated by an analytical equation with only two coefficients.
Knowledge of the solution domain shape permits a few model runs or measurements to be
exploited to define a continuous solution for all slope–azimuth combinations. The shaped
solutions morph over time and environmental gradients.
Keywords: Shaped solution domain, Snow cone, Snow distribution, Snow properties
INTRODUCTION
This research effort arose from a requirement for distributed snow depth and density for use in
high-resolution ground vehicle mobility assessment models used by the U.S. Army (Shoop et al.
2004, Richmond et al. 2005). The requirement was for a realistic distribution of snow depth and
density across the environmental gradients of elevation, slope, azimuth, and forest type. An
approach that is widely applicable and that decreases the dependence on extensive snow property
modeling is needed because the computational and data storage aspects of the mobility model
cannot be overburdened by snow process computations and snow solution storage. Yet, the
distribution of snow properties across a landscape is a complex problem that requires distribution
of mass and energy balance in a high-resolution (30-m) landscape.
One way to visualize this problem is to determine the likely snow depth at a remote point, given
a snow depth at a point on a landscape (Fig. 1). The known point might be where we are standing,
at a low elevation and on a south-facing slope. The point in question may be across the next ridge
on a north-facing slope and at higher elevation. We know there is likely to be more snow at the
remote point, but we do not know how much more. A method for making this extrapolation to an
unknown point (or every point in the landscape) without resorting to extensive calculations would
be quite useful.
1 Correspondence to Rae A. Melloh, U.S. Army Engineer Research and Development Center,
Cold Regions Research and Engineering Laboratory, 72 Lyme Road, Hanover, NH USA 03755-
1290, e-mail rmelloh@crrel.usace.army.mil.
2 U.S. Army Engineer Research and Development Center, Cold Regions Research and
Engineering Laboratory, 72 Lyme Road, Hanover, NH USA 03755-1290.